The use cases could include molecular modelling which would take medical advancements to new heights and enable the creation of new materials. Future quantum computers will be able to help with logistical problems related to optimisation as well as pushing the boundaries and capacity ofartificial intelligence and machine learning. Moreover, the enormous potential for number crunching means that the computers will excel in code breaking and insider security. See below for greater detail.

Quantum Algorithms

“Modelling chemical reactions and materials is one of the most anticipated applications of quantum computing”, the researchers argue. “Instead of spending years, and hundreds of millions of dollars, making and characterising a handful of materials, researchers could study millions of candidates in silicon.”

Computational materials discovery is already a large industry, but the researchers claim that quantum computers promise a radical transition from our current situation. “Chemical-reaction rates are extremely sensitive to molecular energies and span a range wider than classical methods can handle. If robust algorithms are developed, it might be possible to simulate important materials without the overhead of full quantum error correction.”

For example, algorithms are already known that seem to be immune to qubit control errors. A variety of business models could supply quantum simulators. Laboratories might pay a subscription for access. Computing companies could act as consultants. Some businesses might exchange equity in return for quantum-assisted breakthroughs that lead to innovative material developments.

Enhanced Optimization

A central and difficult computational task in all disciplines of physical and social sciences, and across industries, is optimization. And people talk about finding a needle in a haystack, that’s where quantum computing comes in.

“Such problems are difficult to solve with conventional computers because algorithms can navigate only slowly through the mathematical landscape of possible solutions; good solutions may be hidden behind high barriers that are hard to overcome. The most general classical algorithms use statistical methods (such as thermal energy distributions) to ‘jump’ over these barriers.”

We believe that this type of classical sampling could be enhanced by occasionally invoking quantum phenomena such as tunnelling (whereby quantum information is transmitted through barriers) to find rare but high-quality solutions.

“For example, online recommendations and bidding strategies for advertisements use optimization algorithms to respond in the most effective way to consumers’ needs and changing markets,” the researchers claim. “More-powerful protocols, based on a combination of quantum and classical solvers, could improve the quality of products and services in many industries. Logistics companies need to optimize their scheduling, planning and product distribution daily.”

“Quantum enhanced algorithms could improve patient diagnostics for health care. The quality of search or product recommendations for large information-technology companies such as ours, Microsoft, Amazon and Facebook could be enhanced.”

Image portraying a quantum algorithm

Achieving Quantum Supremacy

The researchers also claimed within a few years, an experiment achieving ‘quantum supremacy’ will be performed. This term was coined by theoretical physicist John Preskill to describe the ability of a quantum processor to perform, in a short time, a well-defined mathematical task that even the largest classical supercomputers (such as China’s Sunway TaihuLight) would be unable to complete within any reasonable time frame.

“Among promising applications of quantum sampling are inference and pattern recognition in machine learning. To facilitate experimentation across academia and industry, we plan to offer access to the quantum hardware through a cloud-computing interface.”

What are the Technological Challenges?

Ultimately, the Google researchers state that two main technological challenges must be overcome for quantum computing to be widely commercialised.

Qubits are exceptionally fragile and difficult to keep in stable states. To achieve ‘quantum entanglement’ (coherence), a main requirement for a working machine, researchers must undergo extensive and lengthy error correction for results. Therefore, combining scaling and coherence is the big challenge of quantum systems engineering.

“Quantum hardware needs to be scaled up to compete with classical hardware, which has been improving exponentially for decades.”

Update 2018

Unsurprisingly rapid advancement is steadily being made in this area. Alan Ho, an engineer in Google’s quantum AI lab, revealed the company’s progress at a quantum computing conference in Munich, Germany in June 2017.

His team is is working with a 20-qubit system that has a “two-qubit fidelity” of 99.5 per cent – a measure of how error-prone the processor is, with a higher rating equating to fewer errors. According to the New Scientist, for Google to achieve ‘Quantum Supremacy’, they will need to build a 49-qubit system with a two-qubit fidelity of at least 99.7 per cent. Ho is confident his team will deliver this system in the early months of 2018.

superconducting qubits

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